Principal Simulation Engineer, Industrial Physics and Robotics

At NVIDIA, we build the simulation technologies powering the future of robotics, industrial AI, autonomous systems, and digital twins. NVIDIA Omniverse simulation technologies are used to power reinforcement learning systems, train robots, simulate factories and warehouses, and enable physically accurate digital twins. As simulation becomes foundational to robotics and industrial automation, the demands on physical fidelity are rapidly increasing. Automotive, aerospace, manufacturing, and robotics require simulations that go beyond visual plausibility and reach engineering‑grade predictive accuracy. We are looking for a senior technical leader who can raise the fidelity, robustness, and practical value of our engineering simulation stack for robotics, automation, and industrial digital twins. Rather than focusing on visual plausibility, this is a role for someone who has already built simulation technology used to make decisions about real machines: robots, industrial equipment, vehicles, flexible assemblies, or other complex electromechanical systems. You should be comfortable turning ambiguous real‑world behavior into tractable models, choosing the right level of fidelity for the problem, validating those models against data, and driving production‑quality implementation in a modern agentic AI based accelerated‑computing environment. What You’ll Be Doing

• Design and develop advanced physically based simulation systems for robotics and industrial digital twins.
• Collaborate with teams across PhysX, Newton, Omniverse, and Isaac robotics.
• Bring modern industrial and engineering simulation methodologies into NVIDIA’s scalable simulation stack.
• Design, evaluate and improve methods for multibody dynamics, contact and friction, flexible‑body behavior, deformables, and related numerical solvers.
• Integrate simulation with robotics workflows involving ROS 2, CAD and URDF pipelines, OpenUSD assets, HIL/SIL environments, and controller‑development loops.
• Assist GPU accelerated implementations and scale out of advanced simulation algorithms.
• Mentor engineers and contribute technical leadership across the organization.

• A track record of building or leading simulation software used in production engineering, robotics, industrial machinery, automotive, aerospace, manufacturing, or adjacent domains.
• 15+ years of relevant industry experience, demonstrating sustained impact in simulation development, system design, or engineering software at scale.
• Master’s or PhD degree in a relevant discipline (e.g., mechanical engineering, robotics, computer science, applied mathematics, or physics), or equivalent depth of expertise through professional experience.
• Deep knowledge in several of the following areas: multibody dynamics, constrained systems, contact and friction, articulations, flexible bodies, cable or wire simulation, deformables, FEM, solver design, numerical integration, stiffness handling, or model reduction.
• Experience validating simulators against physical systems, including some combination of test correlation, calibration, HIL/SIL workflows, or controller co‑simulation.
• Working fluency in robotics‑system integration and mechanical‑engineering toolchains, including ROS 2, Simulink, Simscape, and model pipelines based on CAD, URDF, MJCF, or OpenUSD.
• Strong interest in applying modern AI‑assisted and agentic development workflows to large‑scale simulation systems.
• Significant experience developing physically based simulation systems used in production engineering environments.
• Excellent problem‑solving and communication skills.

• Experience with GPU‑accelerated computing (e.g., CUDA, Warp) for high‑performance simulation and parallel workloads.
• Experience with reinforcement learning and sim‑to‑real robotics workflows, including integration with control systems.
• Publications in leading venues such as ICRA, IROS, RSS, SCA, Eurographics, NeurIPS, or related conferences.
• Proven ability to design large‑scale simulation systems that balance numerical robustness, scalability, and performance.
• Familiarity with robotics simulation and control, co‑simulation, model‑based design, neural surrogate models, and platforms such as Omniverse, PhysX, Newton, Isaac Sim & Lab, alongside technical leadership or simulation architecture ownership.

Competitive salaries and a generous benefits package.